CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social Media

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1925–1931 Barcelona, Spain (Online), December 12, 2020. 1925 CyberTronics at SemEval-2020 Task 12: Multilingual Offensive Language Identification over Social Media Sayanta Paul Dept. of CSE IIT Patna, India 1811cs16@iitp.ac.in Sriparna Saha Dept. of CSE IIT Patna, India sriparna@iitp.ac.in Mohammed Hasanuzzaman Dept. of CS CIT Cork, Ireland Mohammed.Hasanuzzaman@cit.ie Abstract The SemEval-2020 Task 12 (OffensEval) challenge focuses on detection of signs of offensiveness using posts or comments over social media. This task has been organized for several languages, e.g., Arabic, Danish, English, Greek and Turkish. It has featured three related sub-tasks for English language: sub-task A was to discriminate between offensive and non-offensive posts, the focus of sub-task B was on the type of offensive content in the post and finally, in sub-task C, proposed systems had to identify the target of the offensive posts. The corpus for each of the languages is developed using the posts and comments over Twitter, a popular social media platform. We have participated in this challenge and submitted results for different languages. The current work presents different machine learning and deep learning techniques and analyzes their performance for offensiveness prediction which involves various classifiers and feature engineering schemes. The experimental analysis on the training set shows that SVM using language specific pre-trained word embedding (Fasttext) outperforms the other methods. Our system achieves a macro-averaged F1 score of 0.45 for Arabic language, 0.43 for Greek language and 0.54 for Turkish language. 1 Introduction Offensive language is very common in social media, now-a-days. Individual users frequently take advantage of the perceived anonymity of computer mediated communication, using this to engage in behavior that many of them would not consider in real life. The SemEval-2020 Task 12 (OffensEval) challenge focuses on prediction of presence of offensive language using the social media. The main goal of this task is to instigate discussion on the creation of reusable benchmarks for evaluating proposed algorithms by exploring issues of evaluation methodology and other processes related to the creation of test collections. The given corpora are developed using the posts and comments over Twitter, a popular social media. Zampieri et al. (2020) organized a multilingual offensive language classification task with a particular focus on Twitter posts. It has released different corpora for the individual languages, e.g., Arabic (Mubarak et al. (2020)), Danish (Sigurbergsson et al. (2020)), English (Rosenthal et al. (2020)), Greek (Pitenis et al. (2020a)) and Turkish (Coltekikin (2020)), and intended to identify and capture the offensive language. All the languages except English have only one task to be performed, e.g., sub-task A which is to identify offensive language. For English, the task has been divided in three different sub-tasks, namely, sub-task B for offense type categorization in which the offense type is categorized into either targeted or untargeted, and sub-task C focuses on identification of target offense. In this paper, different machine learning and deep learning frameworks have been proposed to accom- plish the given task. Each of the proposed systems has …

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